BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. Object-rotation is a classical scanning mode in micro-CT allowing data acquisition from different angles; however, its field-of-view (FOV) is primarily constrained by the size of the detector when aiming for high spatial resolution imaging. Recently, we introduced a novel scanning mode called multiple source translation CT (mSTCT), which effectively enlarges the FOV of the micro-CT system. Furthermore, we developed a virtual projection-based filtered backprojection (V-FBP) algorithm to address truncated projection, albeit with a trade-off in acquisition efficiency (high resolution reconstruction typically requires thousands of source samplings). In this paper, we present a new algorithm for mSTCT reconstruction, backprojection-filtration (BPF), which enables reconstructions of high-resolution images with a low source sampling ratio. Additionally, we found that implementing derivatives in BPF along different directions (source and detector) yields two distinct BPF algorithms (S-BPF and D-BPF), each with its own reconstruction performance characteristics. Through simulated and real experiments conducted in this paper, we demonstrate that achieving same high-resolution reconstructions, D-BPF can reduce source sampling by 75% compared with V-FBP. S-BPF shares similar characteristics with V-FBP, where the spatial resolution is primarily influenced by the source sampling.Comment: 22 pages, 12 figure

Citizens’ Satisfaction with Air Quality and Key Factors in China—Using the Anchoring Vignettes Method

This study uses the anchoring vignettes method to accurately measure citizens’ perceptions of air quality by correcting for the measurement errors which often exist in subjective satisfaction indexes. Our study shows that there is significant variation in satisfaction with air quality before and after using the anchoring vignettes method, especially when calculating and comparing satisfaction levels with the city-level air quality index. In addition, we found that the actual air pollution does indeed decrease citizens’ satisfaction with it, but that the relationship between the two is non-linear. However, among the relevant pollution indicators, citizens are more easily influenced by PM2.5 rather than by SO2 and dust emission concentrations. Finally, our research also found evidence to support the idea that public expectations of air quality in China affect satisfaction levels. Our findings therefore challenge the idea that the relationship between actual and perceived air quality is straightforward, and also confirm that expectation theory holds true for levels of satisfaction with air quality

A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network model and a Mixed Event Extractor

With the development of the Internet, the oversight of research integrity issues has extended beyond the scientific community to encompass the whole of society. If these issues are not addressed promptly, they can significantly impact the research credibility of both institutions and scholars. This article proposes a text convolutional neural network based on SMOTE to identify short texts of potential public opinion events related to suspected scientific integrity issues from common short texts. The SMOTE comprehensive sampling technique is employed to handle imbalanced datasets. To mitigate the impact of short text length on text representation quality, the Doc2vec embedding model is utilized to represent short text, yielding a one-dimensional dense vector. Additionally, the dimensions of the input layer and convolution kernel of TextCNN are adjusted. Subsequently, a short text event extraction model based on TF-IDF and TextRank is proposed to extract crucial information, for instance, names and research-related institutions, from events and facilitate the identification of potential public opinion events related to suspected scientific integrity issues. Results of experiments have demonstrated that utilizing SMOTE to balance the dataset is able to improve the classification results of TextCNN classifiers. Compared to traditional classifiers, TextCNN exhibits greater robustness in addressing the problems of imbalanced datasets. However, challenges such as low information content, non-standard writing, and polysemy in short texts may impact the accuracy of event extraction. The framework can be further optimized to address these issues in the future

A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R³Det

The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R3Det algorithm is proposed in this paper. On the basis of R3Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R3Det and other models

A single locus test for detecting recent positive selection by bi-partitioning the coalescent tree

Many studies have been conducted in the last two decades to search for traces of recent positive selection, and demography is considered as one of the most important confounding factors. To reduce the confounding impact of demography, the coalescent tree topology has been used as a source of information for detecting recent positive selection in a population or a species. Based on the branching pattern of the root, we partition the hypothetical coalescent tree, inferred from a sequence sample, into two subtrees. Positive selection could impose a strong impact on branch length in one of the two subtrees while demography has the same effect on both subtrees. Thus positive selection should be detectable by comparing statistics calculated for the two subtrees. Simulations demonstrate that the proposed test has high power to detect recent positive selection even when DNA polymorphism data from only one locus is available and that it is robust to the confounding impact of demography. One big asset is that all components in the summary statistics (Du) can be computed analytically. Moreover, mis-inference of derived and ancestral alleles should not affect the test and it therefore avoids an annoying problem when detecting recent positive selection

A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R3Det

The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R3Det algorithm is proposed in this paper. On the basis of R3Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R3Det and other models

The Expansion Mechanism of Rural Residential Land and Implications for Sustainable Regional Development: Evidence from the Baota District in China’s Loess Plateau

Rural residential land is the main space of a farmer’s life, rural culture, and social relations. Prior research of rural residential land has focused more on its evolvement in plain and traditional agricultural areas. Yet, there is no clear picture of rural residential land expansion, especially in ecologically fragile areas. This study analyzed the characteristics of rural residential land expansion based on 30 m spatial resolution land-use datasets of the Baota District of Yan’an City, Shannxi Province, and further explored the influencing factors and mechanisms of rural residential land expansion through binary logistic regression (BLR) modeling. Our findings indicated that the area of rural residential land in the Baota District increased by 116.16% during 1990–2015. More than 75% of the residential land expansion came from the occupation of cropland. Moreover, rural residential land expansion was heterogeneous in the rural regional system. The expansion scale, speed, and mode diversity of rural residential land decreased with the increased distance to urban built-up areas. Geographical conditions and resource endowments are the primary internal driving factors; urbanization and policy implementation are two major external driving forces. The authors suggest that the realization of regional sustainable development in ecologically fragile areas should strengthen urban–rural integration, focus on constructing central towns, and ensure ecological protection measures

The Expansion Mechanism of Rural Residential Land and Implications for Sustainable Regional Development: Evidence from the Baota District in China’s Loess Plateau

Rural residential land is the main space of a farmer’s life, rural culture, and social relations. Prior research of rural residential land has focused more on its evolvement in plain and traditional agricultural areas. Yet, there is no clear picture of rural residential land expansion, especially in ecologically fragile areas. This study analyzed the characteristics of rural residential land expansion based on 30 m spatial resolution land-use datasets of the Baota District of Yan’an City, Shannxi Province, and further explored the influencing factors and mechanisms of rural residential land expansion through binary logistic regression (BLR) modeling. Our findings indicated that the area of rural residential land in the Baota District increased by 116.16% during 1990–2015. More than 75% of the residential land expansion came from the occupation of cropland. Moreover, rural residential land expansion was heterogeneous in the rural regional system. The expansion scale, speed, and mode diversity of rural residential land decreased with the increased distance to urban built-up areas. Geographical conditions and resource endowments are the primary internal driving factors; urbanization and policy implementation are two major external driving forces. The authors suggest that the realization of regional sustainable development in ecologically fragile areas should strengthen urban–rural integration, focus on constructing central towns, and ensure ecological protection measures